Artificial Intelligence Nanodegree¶
Convolutional Neural Networks¶
Project: Write an Algorithm for a Dog Identification App¶
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.
Why We're Here¶
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).
In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
The Road Ahead¶
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
- Step 0: Import Datasets
- Step 1: Detect Humans
- Step 2: Detect Dogs
- Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
- Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
- Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
- Step 6: Write your Algorithm
- Step 7: Test Your Algorithm
Step 0: Import Datasets¶
Import Dog Dataset¶
In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:
train_files,valid_files,test_files- numpy arrays containing file paths to imagestrain_targets,valid_targets,test_targets- numpy arrays containing onehot-encoded classification labelsdog_names- list of string-valued dog breed names for translating labels
from sklearn.datasets import load_files
from keras.utils import np_utils
import numpy as np
from glob import glob
###### define function to load train, test, and validation datasets ######
# keras.utils.to_categorical(y, num_classes=None)
# Converts a class vector (integers) to binary class matrix.
# y: class vector to be converted into a matrix (integers from 0 to num_classes).
# num_classes: total number of classes.
# Returns a binary matrix representation of the input.
def load_dataset(path):
data = load_files(path)
dog_files = np.array(data['filenames'])
dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
return dog_files, dog_targets
# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')
# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]
# print statistics about the dataset
print('There are {0} total dog categories.'.format(len(dog_names)))
#print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
#print('There are {0} total dog images.\n'.format(len(np.hstack([train_files, valid_files, test_files]))))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
#print(dog_names)
Import Human Dataset¶
In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.
import random
random.seed(8675309)
# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*")) # /*/* = Load everything that's in the lfw folder
random.shuffle(human_files)
# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
Step 1: Detect Humans¶
We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.
In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
###### extract pre-trained face detector ######
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
# load color (BGR) image
img = cv2.imread(human_files[2])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray, scaleFactor = 1.1)
# print number of faces detected in the image
print('Number of faces detected:', len(faces))
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.
In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.
Write a Human Face Detector¶
We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.
# returns "True" if face is detected by the face_cascade function in image stored at img_path.
def face_detector(img_path, face_cascade):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor = 1.1)
return len(faces) > 0
(IMPLEMENTATION) Assess the Human Face Detector¶
Question 1: Use the code cell below to test the performance of the face_detector function.
- What percentage of the first 100 images in
human_fileshave a detected human face? - What percentage of the first 100 images in
dog_fileshave a detected human face?
Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.
Answer:
98% of the human pictures are detected as humans.
1% of the dog pictures are detected as humans.
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.
import time
# Take the time
start = time.time()
def perfTest(a_face_detector, face_cascade):
## TODO: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
human_detected_as_human = 0
human_detected_as_dog = 0
for i in range(100):
if a_face_detector(human_files_short[i], face_cascade):
human_detected_as_human += 1
elif a_face_detector(dog_files_short[i], face_cascade):
human_detected_as_dog += 1
print("Percentage of human faces detected among the first 100 human pictures: {0}% "
.format(human_detected_as_human/len(human_files_short)*100))
print("Percentage of human faces detected among the first 100 dog pictures: {0}% "
.format(human_detected_as_dog/len(dog_files_short)*100))
#Print the performance
perfTest(face_detector, face_cascade)
# Total time
end = time.time()
print("Total time taken to detect the faces: {0:0.2f}s".format(end-start))
Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?
Answer:
Well, as far as I know, that is usually a quite normal approach to take. Normally we as users are asked to upload a picture where the face can be identified quite clearly. I dont find the expectations to be too hight to set. Besides that, uploading a picture in profile would maybe make less of a sence for example. However, it would be cool to be able to do so in a gaming or just-for-fun app. Just as we did on the CIFAR-10 database pictures, adding reshaped pictures through augmentation and have our algorithm train on those as well might increase to probability to detect less clear ones. Considering shapes and outlines of a face could also increase the reliability (the differences in the shape of the face is quite distinct for humans and dogs). Also, taking into account that the size of the face can vary might increase the hit rate.
However, Haar cascades seems to be a good approach and turns out to be one of the better ones out there. And its free.
Comparing Haar cascades with the LBP cascade below, we see that the latter is more than twice as fast as Haar, but with worse accuracy. One application area might be a toy app for phones where speed might be of more importance for the user.
We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.
## (Optional) TODO: Report the performance of another
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.
# extract pre-trained and downloaded face detector #
face_cascade2 = cv2.CascadeClassifier('lbpcascade/lbpcascade_frontalface_improved.xml')
# load color (BGR) image
img2 = cv2.imread(human_files[2])
# convert BGR image to grayscale
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
# find faces in image
faces2 = face_cascade2.detectMultiScale(gray2, scaleFactor = 1.2)
# print number of faces detected in the image
print('Number of faces detected:', len(faces2))
# get bounding box for each detected face
for (x,y,w,h) in faces2:
# add bounding box to color image
cv2.rectangle(img2,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb2)
plt.show()
# Take the time
start = time.time()
# Print some statistics
perfTest(face_detector, face_cascade2)
# Total time
end = time.time()
print("Total time taken to detect the faces: {0:0.2f}s".format(end-start))
Step 2: Detect Dogs¶
In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.
from keras.applications.resnet50 import ResNet50
# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
Pre-process the Data¶
When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape
$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.
The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape
The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape
Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!
from keras.preprocessing import image
from tqdm import tqdm
def path_to_tensor(img_path):
# loads RGB image as PIL.Image.Image type
img = image.load_img(img_path, target_size=(224, 224))
# convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
x = image.img_to_array(img)
# convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
# (The fourth axis (from 3D to 4D) is added at position axis=0)
return np.expand_dims(x, axis=0)
def paths_to_tensor(img_paths):
list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
return np.vstack(list_of_tensors)
Making Predictions with ResNet-50¶
Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.
Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.
By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.
from keras.applications.resnet50 import preprocess_input, decode_predictions
def ResNet50_predict_labels(img_path):
# returns prediction vector for image located at img_path
img = preprocess_input(path_to_tensor(img_path))
return np.argmax(ResNet50_model.predict(img))
Write a Dog Detector¶
While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).
We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
prediction = ResNet50_predict_labels(img_path)
return ((prediction <= 268) & (prediction >= 151))
(IMPLEMENTATION) Assess the Dog Detector¶
Question 3: Use the code cell below to test the performance of your dog_detector function.
- What percentage of the images in
human_files_shorthave a detected dog? - What percentage of the images in
dog_files_shorthave a detected dog?
Answer:
1% of the humans (in human_files_short) are detected as dogs.
99% of the dogs (in dog_files_short) are detected as dogs.
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
start = time.time()
human_detected_as_dog = 0
dog_detected_as_dog = 0
for i in range(len(human_files_short)):
if dog_detector(human_files_short[i]):
human_detected_as_dog += 1
elif dog_detector(dog_files_short[i]):
dog_detected_as_dog += 1
print("Percentage of humans detected as dogs: {0}%".format(human_detected_as_dog*100/len(human_files_short)))
print("Percentage of dogs detected as dogs: {0}%".format(dog_detected_as_dog*100/len(dog_files_short)))
# Total time
end = time.time()
print("Total time taken to determine the performance: {0:0.2f}s".format(end-start))
Step 3: Create a CNN to Classify Dog Breeds (from Scratch)¶
Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.
Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.
We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.
| Brittany | Welsh Springer Spaniel |
|---|---|
 |
 |
It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).
| Curly-Coated Retriever | American Water Spaniel |
|---|---|
 |
 |
Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.
| Yellow Labrador | Chocolate Labrador | Black Labrador |
|---|---|---|
 |
 |
 |
We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.
Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
Pre-process the Data¶
We rescale the images by dividing every pixel in every image by 255.
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
(IMPLEMENTATION) Model Architecture¶
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
model.summary()
We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:
Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.
Answer: First of all, augment the training data resulted in worse results and took longer time to train, so it was excluded. 10 epochs were tested in both cases. The code can be found below, however, the results are not shown.
The first layer in the CNN must have an input shape of (None, 224, 224, 3) to match the input data we provide it with. The input data constitutes of colour pictures, with the size of 224x224 pixels. Choosing a Conv2D layer with 16 filters to start with, increases the depth of the CNN. From our experience earlier, in the CIFAR-10 database, we know that choosing padding='same' and the activation function='relu' gives us acceptable results (at least sufficient to exceed 1% accuracy), so let's try that again. (Also, it is said in the video that this combination often yields the best results. Given that strides=1.) Adding a MaxPooling2D layer afterwards will decrease the risk of overfitting by reducing the feature map (or spatial dimensions) by half (pool_size=2). I find the GlobalAveragePooling2D layer to reduce the feature map too much, why I choose the MaxPooling2D instead.
Adding another Conv2D layer afterwards and increasing its depth gradually to 32 filters seems like a reasonable next step. Using the same padding and activation function as before, would be in accordance to the CNN we used in the CIFAR-10 case. Slicing the spatial dimension by half again, to prevent overfitting, using a MaxPooling2D layer also seems reasonable.
Adding two Conv2D (filters=64 is the only change) and two MaxPooling2D layers on top of this would increase the number of parameters and the depth, while at the same time decrease the spatial dimension in the CNN. I choose two identical layers here and that's basically just to try it out. In the end, we are just interested in finding a CNN that does slightly better than pure chance. Whether this would be ideal to get higher accuracy is hard to tell (given more epochs). Also, having two layers with 64 filters each rather than one with 64 and another with 128 filters reduces the number of parameters amd, thus, decreased the training time.
Second to the last, we add a GlobalAveragePooling2D layer to reduce the dimensianality to a 2D vector. The Dense layer is added last, with an output layer of 133 (we have 133 different dog breeds to choose between). The activation function='softmax' is chosen to give us the output as a probability.
Create and Configure Augmented Image Generator
#from keras.preprocessing.image import ImageDataGenerator
#train_tensors_to_use = train_tensors
#valid_tensors_to_use = valid_tensors
# create and configure augmented image generator
#datagen_train = ImageDataGenerator(
# width_shift_range=0.2, # randomly shift images horizontally (20% of total width)
# height_shift_range=0.2, # randomly shift images vertically (20% of total height)
# horizontal_flip=True) # randomly flip images horizontally
# create and configure augmented image generator
#datagen_valid = ImageDataGenerator(
# width_shift_range=0.2, # randomly shift images horizontally (20% of total width)
# height_shift_range=0.2, # randomly shift images vertically (20% of total height)
# horizontal_flip=True) # randomly flip images horizontally
# fit augmented image generator on data
#datagen_train.fit(train_tensors_to_use)
#datagen_valid.fit(valid_tensors_to_use)
#train_tensors.shape
Create the CNN Architecture
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, GlobalMaxPooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
from keras.callbacks import ModelCheckpoint
model = Sequential()
### TODO: Define your architecture.
model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu', input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(GlobalAveragePooling2D())
model.add(Dense(133, activation='softmax')) # - 133 different breeds. This is a fully connected layer.
# One can try to add several to increase the accuracy.
# - Adding Dropouts to curb overfitting
# - Stagging multiple convolutional layers before a single max pool
# might increase the accuracy as well.
model.summary()
Compile the Model¶
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
(IMPLEMENTATION) Train the Model¶
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
from keras.callbacks import ModelCheckpoint
import time
### TODO: specify the number of epochs that you would like to use to train the model.
epochs = 10
# Time the training
start = time.time()
### Do NOT modify the code below this line.
##### - Then how will I be able to use the augmented training data??
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5',
verbose=1, save_best_only=True)
model.fit(train_tensors, train_targets,
validation_data=(valid_tensors, valid_targets),
epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
#batch_size=20
#model.fit_generator(datagen_train.flow(train_tensors, train_targets, batch_size=batch_size),
# steps_per_epoch=train_tensors.shape[0] // batch_size,
# epochs=epochs, verbose=2, callbacks=[checkpointer],
# validation_data=datagen_valid.flow(valid_tensors, valid_targets, batch_size=batch_size),
# validation_steps=valid_tensors.shape[0] // batch_size)
# Print out how long it took to train
end = time.time()
tt = end-start
print()
print("Total training time was {0:0.0f} min and {1:0.0f}s. ".format((tt-tt%60)/60, tt%60))
Load the Model with the Best Validation Loss¶
model.load_weights('saved_models/weights.best.from_scratch.hdf5')
Test the Model¶
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]
# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']
Model Architecture¶
The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))
VGG16_model.summary()
Compile the Model¶
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
Train the Model¶
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5',
verbose=1, save_best_only=True)
VGG16_model.fit(train_VGG16, train_targets,
validation_data=(valid_VGG16, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
Load the Model with the Best Validation Loss¶
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')
Test the Model¶
Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]
# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Predict Dog Breed with the Model¶
from extract_bottleneck_features import *
def VGG16_predict_breed(img_path):
# extract bottleneck features
bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
# obtain predicted vector
predicted_vector = VGG16_model.predict(bottleneck_feature)
# return dog breed that is predicted by the model
return dog_names[np.argmax(predicted_vector)]
Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)¶
You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.
In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:
- VGG-19 bottleneck features
- ResNet-50 bottleneck features
- Inception bottleneck features
- Xception bottleneck features
The files are encoded as such:
Dog{network}Data.npz
where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.
(IMPLEMENTATION) Obtain Bottleneck Features¶
In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:
bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']### TODO: Obtain bottleneck features from another pre-trained CNN.
bottleneck_features = np.load('bottleneck_features/DogXceptionData.npz')
train_Xception = bottleneck_features['train']
valid_Xception = bottleneck_features['valid']
test_Xception = bottleneck_features['test']
print(test_Xception.shape)
(IMPLEMENTATION) Model Architecture¶
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
<your model's name>.summary()
Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
Answer:
First of all, I tried all of the bottleneck features. They were all fast to train (roughly 1 minute on a CPU) and far superior (between 47% and 83.6%) to the earlier accuracies obtained. The best result was achieved by the bottleneck feature Xception, with 83.6% correct classifications, after roughly 2,5 min of training. This was later improved to roughly 85.4% through some minor changes, as explained below, with resulting increase in training time.
Given that we already have the trained bottleneck features imported, my idea was to just add the last layers on top of this. That's where the GlobalAveragePooling2D and Dense layers comes from. The input shape of the former needs to match the shape of the input data while the latter output needs to contain 133 different alternatives. However, I added a Dropout layer in between to decrease the risk of overfitting (I planned to run for more baches than before).
Also, it seems that the GlobalAveragePooling2D and GlobalMaxPooling2D layers are the easiest ways to go from a 4D tensor to a 2D. A 2D tensor is needed for the softmax activation function to work. In the end, it seems that the differences in both training time and accuracy are very small between the two, but I stayed with the GlobalMaxPooling2D. Mainly because it was the last one I tried =), but it might have actually reached this accuracy in fewer epochs as well. (Edit afterwards: research seems to suggest that Max Pooling is the better one.)
I also tried a Flatten layer in combination with some Dense layers, with different kind of output shapes. However, the amount of parameters increased so much that I only completed a full run on Dense(64, activation='relu') with roughly 6.4x10^6 parameters. The training took roughly 10 times as long with a resulting ~25% in accuracy. These bad results had me leave this approach, they just didn't seem promising.
As of the compilation step, I choose the same loss function as before but another optimizer. The SGD optimizer, with a very small learning rate, was chosen rather than an adaptive learning rate optimizer. This is to make sure that the updates stay very small so that the previously learned features won't be lost. This, according to the source you provided.
Augment the Training Data
#from keras.preprocessing.image import ImageDataGenerator
#train_Xception_to_use = train_Xception
#valid_Xception_to_use = valid_Xception
# create and configure augmented image generator
#datagen_train = ImageDataGenerator(
# rotation_range = 40,
# width_shift_range=0.2, # randomly shift images horizontally (20% of total width)
# height_shift_range=0.2, # randomly shift images vertically (20% of total height)
# horizontal_flip=True) # randomly flip images horizontally
# create and configure augmented image generator
#datagen_valid = ImageDataGenerator(
# width_shift_range=0.2, # randomly shift images horizontally (20% of total width)
# height_shift_range=0.2, # randomly shift images vertically (20% of total height)
# horizontal_flip=True) # randomly flip images horizontally
# fit augmented image generator on data
#datagen_train.fit(train_Xception_to_use)
#datagen_valid.fit(valid_Xception_to_use)
### TODO: Define your architecture.
Xception_model = Sequential()
#Xception_model.add(Flatten(input_shape=train_Xception.shape[1:]))
#Xception_model.add(Dense(64, activation='relu'))
#Xception_model.add(Dropout(0.5))
#Xception_model.add(Dense(133, activation='softmax'))
#Xception_model.add(GlobalAveragePooling2D(input_shape=train_Xception.shape[1:])) # (7, 7, 512)
Xception_model.add(GlobalMaxPooling2D(input_shape=train_Xception.shape[1:]))
Xception_model.add(Dropout(0.5))
Xception_model.add(Dense(133, activation='softmax'))
Xception_model.summary()
(IMPLEMENTATION) Compile the Model¶
### TODO: Compile the model.
from keras import optimizers
#Xception_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
Xception_model.compile(loss='categorical_crossentropy',
optimizer = optimizers.SGD(lr=1e-4, momentum=0.9),
metrics=['accuracy'])
(IMPLEMENTATION) Train the Model¶
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
# Time the training
start = time.time()
### TODO: Train the model.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Xception.hdf5',
verbose=1, save_best_only=True)
Xception_model.fit(train_Xception, train_targets,
validation_data=(valid_Xception, valid_targets),
epochs=100, batch_size=20, callbacks=[checkpointer], verbose=1)
# Print how long it took
end = time.time()
tt = end-start
print("\nTotal training time was {0:0.0f} min and {1:0.0f} s. ".format((tt-tt%60)/60, tt%60))
(IMPLEMENTATION) Load the Model with the Best Validation Loss¶
### TODO: Load the model weights with the best validation loss.
Xception_model.load_weights('saved_models/weights.best.Xception.hdf5')
(IMPLEMENTATION) Test the Model¶
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.
### TODO: Calculate classification accuracy on the test dataset.
# get index of predicted dog breed for each image in test set
Xception_predictions = [np.argmax(Xception_model.predict(np.expand_dims(feature, axis=0)))
for feature in test_Xception]
# report test accuracy
test_accuracy = (100*np.sum(np.array(Xception_predictions)==np.argmax(test_targets, axis=1))/
len(Xception_predictions))
print('Test accuracy: %.4f%%' % test_accuracy)
(IMPLEMENTATION) Predict Dog Breed with the Model¶
Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.
Similar to the analogous function in Step 5, your function should have three steps:
- Extract the bottleneck features corresponding to the chosen CNN model.
- Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
- Use the
dog_namesarray defined in Step 0 of this notebook to return the corresponding breed.
The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function
extract_{network}
where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
def Xception_predict_breed(img_path):
# extract bottleneck features
Xception_bottleneck_feature = extract_Xception(path_to_tensor(img_path))
# get the predicted vector
predicted_vector = Xception_model.predict(Xception_bottleneck_feature)
# return the predicted dog breed
return dog_names[np.argmax(predicted_vector)]
Step 6: Write your Algorithm¶
Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,
- if a dog is detected in the image, return the predicted breed.
- if a human is detected in the image, return the resembling dog breed.
- if neither is detected in the image, provide output that indicates an error.
You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.
Some sample output for our algorithm is provided below, but feel free to design your own user experience!
(IMPLEMENTATION) Write your Algorithm¶
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
# I have tried to display the predicted dog breed as well but without success. =(
def import_files():
files_to_check = np.array(glob("images/check_this/*")) # Make sure that the images are JPEG!
#files_to_check = np.array(glob("dogImages/valid/107.Norfolk_terrier"))
#print(files_to_check)
return files_to_check # if not, the kernel will stop running.
def get_dog_breed_picture(breed):
length = len(breed)
for item in glob("dogImages/valid/*/*"):
print(item[-(length + 10): -10])
if item[-(length + 10): -10] == breed:
print(item)
breed_pics = np.array(glob(item))
breed_pic = cv2.imread(item)
cv_rgb2 = cv2.cvtColor(item, cv2.COLOR_BGR2RGB)
return cv_rgb2
def image_detector(file):
# start by displaying the picture
image = cv2.imread(file)
cv_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.figure()
#plt.figure(figsize=(10, 10))
#plt.subplot(2,2,1)
plt.imshow(cv_rgb)
plt.show()
# return the breed
breed = Xception_predict_breed(file)
# check whether the picture is a dog, human or neither
if dog_detector(file):
print("Dog breed: {0}".format(breed))
elif face_detector(file, face_cascade):
#dog_breed_pucture = get_dog_breed_picture(breed)
#plt.subplot(2,2,2)
#plt.imshow(dog_breed_pucture)
print("Hey human!")
print("You look like the dog breed: {0}".format(breed))
print("Don't worry however, it's cute!")
else:
print("It seems that I can't classify this picture.")
start = time.time()
images = import_files()
image_detector(images[0])
end = time.time()
tt = end-start
print("\nTime to run the algorithm: {0:0.0f} min and {1:0.0f} s ".format((tt-tt%60)/60, tt%60))
Step 7: Test Your Algorithm¶
In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?
(IMPLEMENTATION) Test Your Algorithm on Sample Images!¶
Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.
Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
Answer:
Well, it classifies 8 out of 10 pictures correct, so I'm quite happy (see the output down below). It has problems to classify the third to last picture as a human. Maybe it's because of the sunglasses, maybe for other reasons. Also, the last dog picture is wrongly classified as an American Foxhound when it's actually a Dachshund, if the source is correct. However they are two breeds very similar to each other. Also, it does a good job with the cat picture.
The dog breed classifier does it job well in roughly 85% of the times. It's more than acceptable, but maybe little less than I thought before starting the project. Something I'm less happy with is the speed of the algorithm, it takes way too long to classify each picture. I'm running on a normal macbook pro, so I won't be able to use the GPU during training, but during classifying that shouldn't make any difference. If I understand it correct. It's a quite fast laptop, so it makes me wonder if I can change the algorithm to make the process faster? One way would be to use the other face detector (face_cascade2), but I would have to pay in accuracy, also it wouldn't be relevant for the dog pictures.
(Question for the reviewer:) Is it supposed to be this slow?
Some improvements to make in order to increase the accuracy:
- Augment the input data and let the model train on it as well. However, I tried this in the initial-from-scratch model without any obvious improvements. But in general, this wold be a valid approach.
- Trying different architectures and fine tune the parameters would eventually most likely result in better performance as well. I've tried some ten different architectures, but the possible alternatives are so many that there are most likely many that outperforms the one I chose.
- Use more training and validation data. We have in total 8351 dog and 13233 human images. It's a fairly small data set compared to the data that's actually out there and to the amount of data Google is training their algorithms on for example.
- I would also make sure that the face detector alorithm takes into account face shapes in a larger extent to prevent the missclassified human.
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
for i in range(len(images)):
image_detector(images[i])
print("="*80)
